Robust Physics-Guided Diffusion for Full-Waveform Inversion
This work addresses full-waveform inversion for geophysical imaging, presenting an incremental improvement through a novel diffusion-based method.
The paper tackles the problem of full-waveform inversion by developing a robust physics-guided diffusion framework that combines a score-based generative prior with wave-equation simulations, achieving improved reconstruction quality over deterministic optimization baselines and standard diffusion posterior sampling on OpenFWI datasets.
We develop a robust physics-guided diffusion framework for full-waveform inversion that combines a score-based generative prior with likelihood guidance computed through wave-equation simulations. We adopt a transport-based data-consistency potential (Wasserstein-2), incorporating wavefield enhancement via bounded weighting and observation-dependent normalization, thereby improving robustness to amplitude imbalance and time/phase misalignment. On the inference side, we introduce a preconditioned guided reverse-diffusion scheme that adapts the guidance strength and spatial scaling throughout the reverse-time dynamics, yielding a more stable and effective data-consistency guidance step than standard diffusion posterior sampling (DPS). Numerical experiments on OpenFWI datasets demonstrate improved reconstruction quality over deterministic optimization baselines and standard DPS under comparable computational budgets.